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<html lang="en"> | |
<head> | |
<meta charset="UTF-8"> | |
<title>Zero Shot Image Classification - Hugging Face Transformers.js</title> | |
<script type="module"> | |
// Import the library | |
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]'; | |
// Make it available globally | |
window.pipeline = pipeline; | |
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<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet"> | |
<link rel="stylesheet" href="css/styles.css"> | |
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<body> | |
<div class="container-main"> | |
<!-- Page Header --> | |
<div class="header"> | |
<div class="header-logo"> | |
<img src="images/logo.png" alt="logo"> | |
</div> | |
<div class="header-main-text"> | |
<h1>Hugging Face Transformers.js</h1> | |
</div> | |
<div class="header-sub-text"> | |
<h3>Free AI Models for JavaScript Web Development</h3> | |
</div> | |
</div> | |
<hr> <!-- Separator --> | |
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<!-- Content --> | |
<div class="container mt-5"> | |
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<div class="text-center"> | |
<h2>Computer Vision</h2> | |
<h4>Zero Shot Image Classification</h4> | |
</div> | |
<!-- Actual Content of this page --> | |
<div id="zero-shot-image-classification-container" class="container mt-4"> | |
<h5>Zero Shot Image Classification w/ Xenova/clip-vit-base-patch32:</h5> | |
<div class="d-flex align-items-center mb-2"> | |
<label for="zeroShotImageClassificationURLText" class="mb-0 text-nowrap" | |
style="margin-right: 15px;">Enter | |
image URL:</label> | |
<input type="text" class="form-control flex-grow-1" id="zeroShotImageClassificationURLText" | |
value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg" | |
placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;"> | |
</div> | |
<div class="d-flex align-items-center"> | |
<label for="labelsText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Labels (comma | |
separated):</label> | |
<input type="text" class="form-control flex-grow-1" id="labelsText" value="tiger, horse, dog" | |
placeholder="Enter labels (comma separated)" style="margin-right: 15px; margin-left: 15px;"> | |
<button id="classifyButton" class="btn btn-primary ml-2" onclick="classifyImage()">Classify</button> | |
</div> | |
<div class="mt-4"> | |
<h4>Output:</h4> | |
<pre id="outputArea"></pre> | |
</div> | |
</div> | |
<hr> <!-- Line Separator --> | |
<div id="zero-shot-image-classification-local-container" class="container mt-4"> | |
<h5>Zero Shot Image Classification Local File:</h5> | |
<div class="d-flex align-items-center mb-2"> | |
<label for="imageClassificationLocalFile" class="mb-0 text-nowrap" | |
style="margin-right: 15px;">Select Local Image:</label> | |
<input type="file" id="imageClassificationLocalFile" accept="image/*" /> | |
</div> | |
<div class="d-flex align-items-center"> | |
<label for="labelsLocalText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Labels (comma | |
separated):</label> | |
<input type="text" class="form-control flex-grow-1" id="labelsLocalText" value="tiger, horse, dog" | |
placeholder="Enter labels (comma separated)" style="margin-right: 15px; margin-left: 15px;"> | |
<button id="classifyLocalButton" class="btn btn-primary ml-2" onclick="classifyLocalImage()">Classify</button> | |
</div> | |
<div class="mt-4"> | |
<h4>Output:</h4> | |
<pre id="outputAreaLocal"></pre> | |
</div> | |
</div> | |
</div> | |
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<div class="row mt-5"> | |
<div class="col-md-12 text-center"> | |
<a href="index.html" class="btn btn-outline-secondary" | |
style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a> | |
</div> | |
</div> | |
</div> | |
</div> | |
<script> | |
let classifier; | |
// Initialize the sentiment analysis model | |
async function initializeModel() { | |
classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32'); | |
} | |
async function classifyImage() { | |
const textFieldValue = document.getElementById("zeroShotImageClassificationURLText").value.trim(); | |
const labels = document.getElementById("labelsText").value.trim().split(",").map(item => item.trim()); | |
const result = await classifier(textFieldValue, labels); | |
document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2); | |
} | |
async function classifyLocalImage() { | |
const fileInput = document.getElementById("imageClassificationLocalFile"); | |
const file = fileInput.files[0]; | |
if (!file) { | |
alert('Please select an image file first.'); | |
return; | |
} | |
// Create a Blob URL from the file | |
const url = URL.createObjectURL(file); | |
const labels = document.getElementById("labelsLocalText").value.trim().split(",").map(item => item.trim()); | |
const result = await classifier(url, labels); | |
document.getElementById("outputAreaLocal").innerText = JSON.stringify(result, null, 2); | |
} | |
// Initialize the model after the DOM is completely loaded | |
window.addEventListener("DOMContentLoaded", initializeModel); | |
</script> | |
</body> | |
</html> |